In this work, all methods are implemented, trained, and tested by PyTorch 1.8 [1]} on a cluster with eight NVIDIA GTX1080TI GPUs. We choice the powerful nnUNet [2]} as our baseline for fair comparisons. As it is a self-configuration segmentation framework without needing any manual effort for data processing, training planning (network architectures and parameters setting, etc), and post-processing, and has won more than 19 medical segmentation challenges [2]}. Due to the nnUNet just provides the vanilla UNet network, we further adapt it to support more network architectures. Note that, we use the public implementations of the compared methodshttps://github.com/qubvel/segmentation_models.pytorch. We employ the default settings of nnUNet as our experimental settings, where the batch size is 2 for 3D methods and 12 for 2D methods, the total epoch is 1000 and the loss function is a combination of cross-entropy and dice loss. All the models are trained and tested based on the default settings, except we don't use the test time augmentation. And, each model needs more than six GPU days to train and each volume takes more than five minutes to inference. In this work, we use two widely-used metrics to measure the segmentation quality. Dice similarity coefficient (\(DSC\) ) is used to evaluate the pixel-wise overlap between the ground truth and prediction, higher is better. In addition, we also measure the 95% Hausdorff Distance (\(HD_{95}\) ) between the boundaries of the ground truth and prediction, lower is better. The implementations of \(DSC\)  and \(HD_{95}\)  are availablehttps://github.com/loli/medpy.
